With the continuous improvement of state perception and self description capabilities of secondary equipment in intelligent substations, it not only improves fine-grained regulation of the power grid, but also doubles the difficulty of fault diagnosis because of its massive, complex, and discrete state information. To improve the accuracy and efficiency of secondary equipment fault diagnosis, a secondary equipment fault diagnosis algorithm based on a relational hypergraph-enhanced Transformer is proposed. First, a Priori algorithm is used to mine the association rules between fault signals and a relationship hypergraph is constructed. Then, a hypergraph convolutional neural network (HGCN) and a fine-tuned standard Transformer network are used to learn high-order relationships and contextual expressions between fault features, and then fault types are predicted through error backpropagation and a nonlinear transfer function. Finally, the annual operational data of secondary equipment in a certain region is taken as an example for analysis. The results show that the proposed method can remove redundant information interference, accurately locate faulty components and diagnose fault types, providing support for intelligent operation and maintenance.